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Refactor fixed effect solver into its own function

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Thomas A. Christensen II 2021-06-19 19:56:32 -05:00
parent f5f1dfad13
commit 181819db28
Signed by: millironx
GPG key ID: 139C07724802BC5D
1 changed files with 69 additions and 59 deletions
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128
src/BeefBLUP.jl
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@ -71,65 +71,7 @@ function beefblup(path::String, savepath::String, h2::Float64)
# Extract all of the fixed effects
fixedfx = select(data, Not([:id, :birthdate, :sire, :dam]))[:,1:end - 1]
# Find any columns that need to be deleted
for i in 1:ncol(fixedfx)
if length(unique(fixedfx[:,i])) <= 1
@warn string("column '", names(fixedfx)[i], "' does not have any unique animals and will be removed from this analysis")
DataFrames.select!(fixedfx, Not(i))
end
end
# Determine how many contemporary groups there are
numtraits = ncol(fixedfx)
numgroups = ones(1, numtraits)
for i in 1:numtraits
numgroups[i] = length(unique(fixedfx[:,i]))
end
# If there are more groups than animals, then the analysis cannot continue
if sum(numgroups) >= numanimals
throw(ErrorException("there are more contemporary groups than animals"))
end
# Define a "normal" animal as one of the last in the groups, provided that
# all traits do not have null values
normal = Array{String}(undef, 1, numtraits)
for i in 1:numtraits
for j in numanimals:-1:1
if !ismissing(fixedfx[j,i])
normal[i] = string(fixedfx[j,i])
break
end
end
end
# Form the fixed-effect matrix
X = zeros(Int8, numanimals, floor(Int, sum(numgroups)) - length(numgroups) + 1)
X[:,1] = ones(Int8, 1, numanimals)
# Create an external counter that will increment through both loops
counter = 2
# Store the traits in a string array
adjustedtraits =
Array{String}(undef,floor(Int, sum(numgroups)) - length(numgroups))
# Iterate through each group
for i in 1:length(normal)
# Find the traits that are present in this trait
localdata = string.(fixedfx[:,i])
traits = unique(localdata)
# Remove the normal version from the analysis
effecttraits = traits[findall(x -> x != normal[i], traits)]
# Iterate inside of the group
for j in 1:(length(effecttraits))
matchedindex = findall(x -> x == effecttraits[j], localdata)
X[matchedindex, counter] .= 1
# Add this trait to the string
adjustedtraits[counter - 1] = traits[j]
# Increment the big counter
counter = counter + 1
end
end
(X, numgroups, normal, adjustedtraits) = fixedeffectmatrix(fixedfx)
# Create an empty matrix for the additive relationship matrix
A = zeros(numanimals, numanimals)
@ -261,4 +203,72 @@ function beefblup(path::String, savepath::String, h2::Float64)
close(fileID)
end
function fixedeffectmatrix(fixedeffects::AbstractDataFrame)
# Find any columns that need to be deleted
for i in 1:ncol(fixedeffects)
if length(unique(fixedeffects[:,i])) <= 1
@warn string("column '", names(fixedeffects)[i], "' does not have any unique animals and will be removed from this analysis")
DataFrames.select!(fixedeffects, Not(i))
end
end
# Determine how many contemporary groups there are
numtraits = ncol(fixedeffects)
numgroups = ones(1, numtraits)
for i in 1:numtraits
numgroups[i] = length(unique(fixedeffects[:,i]))
end
# If there are more groups than animals, then the analysis cannot continue
numanimals = length(fixedeffects[:,1])
if sum(numgroups) >= numanimals
throw(ErrorException("there are more contemporary groups than animals"))
end
# Define a "normal" animal as one of the last in the groups, provided that
# all traits do not have null values
numtraits = ncol(fixedeffects)
numanimals = length(fixedeffects[:,1])
normal = Array{String}(undef, 1, numtraits)
for i in 1:numtraits
for j in numanimals:-1:1
if !ismissing(fixedeffects[j,i])
normal[i] = string(fixedeffects[j,i])
break
end
end
end
# Form the fixed-effect matrix
X = zeros(Int8, numanimals, floor(Int, sum(numgroups)) - length(numgroups) + 1)
X[:,1] = ones(Int8, 1, numanimals)
# Create an external counter that will increment through both loops
counter = 2
# Store the traits in a string array
adjustedtraits =
Array{String}(undef,floor(Int, sum(numgroups)) - length(numgroups))
# Iterate through each group
for i in 1:length(normal)
# Find the traits that are present in this trait
localdata = string.(fixedeffects[:,i])
traits = unique(localdata)
# Remove the normal version from the analysis
effecttraits = traits[findall(x -> x != normal[i], traits)]
# Iterate inside of the group
for j in 1:(length(effecttraits))
matchedindex = findall(x -> x == effecttraits[j], localdata)
X[matchedindex, counter] .= 1
# Add this trait to the string
adjustedtraits[counter - 1] = traits[j]
# Increment the big counter
counter = counter + 1
end
end
return X, numgroups, normal, adjustedtraits
end
end